Difference between revisions of "Class meeting for 10-605 Graph Architectures for ML"
From Cohen Courses
Jump to navigationJump to search (Created page with "This is one of the class meetings on the schedule for the course Machine Learning with Large Datase...") |
(→Slides) |
||
| Line 4: | Line 4: | ||
| − | * | + | * Slides [http://www.cs.cmu.edu/~wcohen/10-605/graph-arch.pptx in powerpoint], [http://www.cs.cmu.edu/~wcohen/10-605/graph-arch.pptx in pdf] |
=== Readings === | === Readings === | ||
Revision as of 13:59, 9 November 2016
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall 2016.
Slides
- Slides in powerpoint, in pdf
Readings
- None required. Optional: Philip Stutz, Abraham Bernstein and William W. Cohen (2010): Signal/Collect: Graph Algorithms for the (Semantic) Web in ISWC-2010. Longer version is also available.
Things to Remember and/or Think About
- Differences and commonalities between the graph-processing methods discussed: Pregel, Signal-Collect, GraphX, and GraphChi.
- How graph-processing approaches could be used for ML problems studied in class, like SSL, PIC, and LDA.
- How operations on graphs can be implemented in dataflow languages.
